Latin hypercube approach to estimate uncertainty in ground water vulnerability

Area of Science:

• Environmental science
• Hydrogeology
• Geographic Information Systems (GIS)

Background:

• Groundwater vulnerability models are crucial for assessing contamination risks.
• Existing models often lack robust methods for quantifying prediction uncertainty.
• Uncertainty arises from model coefficients and spatial data errors.

Purpose of the Study:

• To propose and illustrate a methodology for quantifying prediction uncertainty in groundwater vulnerability models.
• To integrate multivariate logistic regression with GIS for enhanced vulnerability assessment.
• To address uncertainty stemming from both model and data errors.

Main Methods:

• Coupling multivariate logistic regression with GIS.
• Employing Latin hypercube sampling (LHS) to simulate input error propagation.
• Developing probability distributions to represent prediction intervals and uncertainty.

Main Results:

• Significant spatial variations in prediction uncertainty were identified across the High Plains aquifer.
• The proposed method successfully illustrates the propagation of input errors.
• Prediction intervals and associated uncertainties were quantified.

Conclusions:

• The developed methodology provides a robust framework for uncertainty quantification in groundwater vulnerability models.
• Spatial deconstruction of uncertainty aids in refining vulnerability predictions.
• This approach enhances the reliability of groundwater resource management strategies.